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Design-MCP — Awwwards UI/UX Knowledge Base + MCP Server

An autonomous build: a data pipeline that cleans, chunks, tags, and embeds thousands of premium static websites into a vector database, plus a custom MCP server that lets any AI coding agent semantically retrieve world-class UI components and adapt them into clean React + Tailwind.

How to run this project with Claude Code

This repo is set up to be built by an agent. Open it in Claude Code and type:

/start

The orchestrator reads .claude/state/progress.json, figures out the next incomplete phase, and dispatches the matching subagent. It loops: run phase → QA verifier gates it → mark done → next phase. Say /start again anytime to resume from where it stopped. Other commands: /status, /run-phase <n>, /verify.

Related MCP server: aceternityui-mcp

Pipeline (one command, resumable)

npm install
cp .env.example .env      # fill in keys
npm run pipeline          # clean -> chunk -> tag -> embed -> upload
npm run server            # start the MCP server on stdio

Architecture

Four phases, each owned by a dedicated subagent, gated by a QA verifier:

Phase

Owner subagent

Output

1. Clean + Chunk

data-pipeline-engineer

clean component chunks

2. Tag

data-pipeline-engineer

.metadata.json per chunk

3. Embed + Store

vector-db-engineer

vectors in Mongo Atlas

4. MCP Server

mcp-server-engineer

search_premium_ui tool

—. Wire agent

integration-engineer

client config + persona

See docs/ARCHITECTURE.md for the full engineering blueprint and CLAUDE.md for the rules every agent follows.

Design principles

  • Zero manual processing — no hand-cleaning of folders.

  • Resumable + idempotent — every stage skips already-done work; re-runs are safe.

  • Bounded concurrency — a worker pool (src/lib/pool.ts) keeps memory flat over thousands of files and respects LLM rate limits.

  • Adapt, don't copy — the agent rebuilds retrieved UI as modular React, never pastes.

Tool Schema Changelog

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